Large Geographic Scales Research Articles

Using graph theory and social media data to assess cultural ecosystem services in coastal areas: Method development and application

The use of social media (SM) data has emerged as a promising tool for the assessment of cultural ecosystem services (CES). Most studies have focused on the use of single SM platforms and on the analysis of photo content to assess the demand for CES. Here, we introduce a novel methodology for the assessment of CES using SM data through the application of graph theory network analyses (GTNA) on hashtags associated to SM posts and compare it to photo content analysis. We applied the proposed methodology on two SM platforms, Instagram and Twitter, on three worldwide known case study areas, namely Great Barrier Reef, Galapagos Islands and Easter Island. Our results indicate that the analysis of hashtags through graph theory offers similar capabilities to photo content analysis in the assessment of CES provision and the identification of CES providers. More importantly, GTNA provides greater capabilities at identifying relational values and eudaimonic aspects associated to nature, elusive aspects for photo content analysis. In addition, GTNA contributes to the reduction of the interpreter’s bias associated to photo content analyses, since GTNA is based on the tags provided by the users themselves. The study also highlights the importance of considering data from different SM platforms, as the type of users and the information offered by these platforms can show different CES attributes. The ease of application and relative short computing processing times involved in the application of GTNA makes it a cost-effective method with the potential of being applied to large geographical scales.

Geographic Linkages of Root Traits to Salt Marsh Productivity

Root traits are fundamental characteristics of belowground ecosystems that regulate plant growth and drive ecosystem functioning. Nevertheless, the way root traits respond to environmental factors and consequently influence productivity remains unexplored on large geographic scales. We examined the root traits of exotic Spartina alterniflora and native Phragmites australis across China’s coastal salt marshes. Using structural equation models (SEMs), we quantified the direct and indirect effects of mean annual temperature, soil nutrients (e.g., soil dissolved inorganic nitrogen and phosphorus), and root traits on aboveground net primary productivity. Our results showed that root traits of S. alterniflora were more sensitive to changing soil nutrient availability than those of P. australis. The SEMs indicated that soil nutrient availability increased S. alterniflora productivity by increasing root nitrogen concentration and root length density. In P. australis, temperature could increase productivity by both increasing root length density and soil nutrient-mediated root nitrogen concentration. The studied root architectural trait (root length density) and nutrient trait (root nitrogen concentration) were effective in predicting productivity, whereas none of the root morphological traits (i.e., specific root length, root tissue density, and root diameter) significantly affected productivity. We provide the first empirical evidence that root trait-based responses modulate the effects of climate and soil nutrients on geographic variation in vegetation productivity, but these effects are species specific.

Genetic consequences of social structure in the golden-crowned sifaka

Many species are structured in social groups (SGs) where individuals exhibit complex mating strategies. Yet, most population genetic studies ignore SGs either treating them as small random-mating units or focusing on a higher hierarchical level (the population). Empirical studies acknowledging SGs have found an overall excess of heterozygotes within SGs and usually invoke inbreeding avoidance strategies to explain this finding. However, there is a lack of null models against which ecological theories can be tested and inbreeding avoidance quantified. Here, we investigate inbreeding (deviation from random mating) in an endangered forest-dwelling pair-living lemur species (Propithecus tattersalli). In particular, we measure the inbreeding coefficient (FIS) in empirical data at different scales: SGs, sampling sites and forest patches. We observe high excess of heterozygotes within SGs. The magnitude of this excess is highly dependent on the sampling scheme: while offspring are characterised by a high excess of heterozygotes (FIS < 0), the reproductive pair does not show dramatic departures from Hardy–Weinberg expectations. Moreover, the heterozygosity excess disappears at larger geographic scales (sites and forests). We use a modelling framework that incorporates details of the sifaka mating system but does not include active inbreeding avoidance mechanisms. The simulated data show that, although apparent “random mating” or even inbreeding may occur at the “population” level, outbreeding is maintained within SGs. Altogether our results suggest that social structure leads to high levels of outbreeding without the need for active inbreeding avoidance mechanisms. Thus, demonstrating and measuring the existence of active inbreeding avoidance mechanisms may be more difficult than usually assumed.

Contrasting patterns of population divergence on young and old landscapes inBanksia seminuda(Proteaceae), with evidence for recognition of subspecies

AbstractOCBIL theory provides a basis for understanding of the evolution and ecology of biota on old, climatically buffered, infertile landscapes (OCBILs) worldwide. Here, we test a genetic hypothesis presented in OCBIL theory that predicts highly differentiated intraspecific population systems on OCBILs vs. more limited differentiation on young, often disturbed, fertile landscapes (YODFELs). We examined patterns of genomic and morphological divergence in Banksia seminuda across OCBILs and YODFELs in south-western Australia. We also used these data to determine whether these OCBIL and YODFEL populations represent distinct subspecific lineages, a point of previous contention among taxonomists. As hypothesized, genomic analyses based on 3466 SNP loci revealed strong structuring within B. seminuda, with high differentiation across narrow geographic scales among OCBIL populations vs. lower differentiation across much larger geographic scales among YODFEL populations. In addition, genomic and morphological divergence was found between OCBIL and YODFEL populations, providing comprehensive quantitative evidence for two subspecies. These findings have taxonomic implications for the species and provide support for OCBIL theory and its insights into the evolution, ecology and conservation of biota on ancient landscapes.

Large-scale genetic admixture suggests high dispersal in an insect pest, the apple fruit moth.

Knowledge about population genetic structure and dispersal capabilities is important for the development of targeted management strategies for agricultural pest species. The apple fruit moth, Argyresthia conjugella (Lepidoptera, Yponomeutidae), is a pre-dispersal seed predator. Larvae feed on rowanberries (Sorbus aucuparia), and when rowanberry seed production is low (i.e., inter-masting), the moth switches from laying eggs in rowanberries to apples (Malus domestica), resulting in devastating losses in apple crops. Using genetic methods, we investigated if this small moth expresses any local genetic structure, or alternatively if gene flow may be high within the Scandinavian Peninsula (~850.000 km2, 55o - 69o N). Genetic diversity was found to be high (n = 669, mean He = 0.71). For three out of ten tetranucleotide STRs, we detected heterozygote deficiency caused by null alleles, but tests showed little impact on the overall results. Genetic differentiation between the 28 sampling locations was very low (average FST = 0.016, P < 0.000). Surprisingly, we found that all individuals could be assigned to one of two non-geographic genetic clusters, and that a third, geographic cluster was found to be associated with 30% of the sampling locations, with weak but significant signals of isolation-by-distance. Conclusively, our findings suggest wind-aided dispersal and spatial synchrony of both sexes of the apple fruit moth over large areas and across very different climatic zones. We speculate that the species may recently have had two separate genetic origins caused by a genetic bottleneck after inter-masting, followed by rapid dispersal and homogenization of the gene pool across the landscape. We suggest further investigations of spatial genetic similarities and differences of the apple fruit moth at larger geographical scales, through life-stages, across inter-masting, and during attacks by the parasitoid wasp (Microgaster politus).

Occurrence and risk assessment of microcystin and its relationship with environmental factors in lakes of the eastern plain ecoregion, China.

The frequent occurrence of microcystins (MCs) in freshwater poses serious threats to the drinking water safety and health of human beings. Although MCs have been detected in individual fresh waters in China, little is known about their occurrence over a large geographic scale. An investigation of 30 subtropical lakes in eastern China was performed during summer 2018 to determine the MCs concentrations in water and their possible risk via direct water consumption to humans, and to assess the associated environmental factors. MCs were detected in 28 of 30 lakes, and the highest mean MCs concentrations occurred in Lake Chaohu (26.7μg/L), followed by Lake Taihu (3.11μg/L). MC-LR was the primary variant observed in our study, and MCs were mainly produced by Microcystis, Anabaena (Dolicospermum), and Oscillatoria in these lakes. Replete nitrogen and phosphorus concentrations, irradiance, and stable water column conditions were critical for dominance of MC-producing cyanobacteria and high MCs production in our study. Hazard quotients indicated that human health risk of MCs in most lakes was at moderate or low levels except Lakes Chaohu and Taihu. Nutrient control management is recommended to decrease the likelihood of high MCs production. Finally, we recommend the regional scale thresholds of total nitrogen and total phosphorus concentrations of 1.19mg/L and 7.14 × 10-2mg/L, respectively, based on the drinking water guideline of MC-LR (1μg/L) recommended by World Health Organization. These targets for nutrient control will aid water quality managers to reduce human health risks created by exposure to MCs.

A large-scale assessment of lakes reveals a pervasive signal of land use on bacterial communities.

Lakes play a pivotal role in ecological and biogeochemical processes and have been described as "sentinels" of environmental change. Assessing "lake health" across large geographic scales is critical to predict the stability of their ecosystem services and their vulnerability to anthropogenic disturbances. The LakePulse research network is tasked with the assessment of lake health across gradients of land use on a continental scale. Bacterial communities are an integral and rapidly responding component of lake ecosystems, yet large-scale responses to anthropogenic activity remain elusive. Here, we assess the ecological impact of land use on bacterial communities from over 200 lakes covering more than 660,000 km2 across Eastern Canada. In addition to community variation between ecozones, land use across Eastern Canada also appeared to alter diversity, community composition, and network structure. Specifically, increasing anthropogenic impact within the watershed lowered diversity. Likewise, community composition was significantly correlated with agriculture and urban development within a watershed. Interaction networks showed decreasing complexity and fewer keystone taxa in impacted lakes. Moreover, we identified potential indicator taxa of high or low lake water quality. Together, these findings point to detectable bacterial community changes of largely unknown consequences induced by human activity within lake watersheds.

Distribution of Clam Shrimps (Crustacea: Laevicaudata and Spinicaudata) in South Africa, with New Records from the Northern Cape Province.

The ephemeral waterbodies of southern Africa are regarded a global hotspot for large Branchiopod diversity. Although the distributions and systematics of Anostraca and Notostraca have been fairly well defined, clam shrimps have received much less attention. So far, 18 clam shrimp species are known from the sub-region, but none of the available published literature defines their distribution in South Africa. Furthermore, most of the recent studies were concentrated in the mesic provinces, while very little information is available from the Northern Cape, where most ephemeral waterbodies in the country occur. This study reviews the distribution of clam shrimps in South Africa by reviewing published distribution records and contributing novel data from surveys in the Northern Cape. We found that 13 of the 18 species from the sub-region occur in South Africa, of which four are restricted to their respective provinces. We further clarify the current state of endemism patterns in South Africa and provide novel findings from the Northern Cape, including three new range extensions. The Northern Cape hosts the highest species richness, with nine species, followed by the Eastern Cape, where seven species have been recorded so far. Most other provinces have low species richness and endemism, while no species records have been published from the Limpopo province yet. Surveys over large geographical scales are important, and more research is needed on clam shrimp systematics in South Africa.

Climatic temperature controls the geographical patterns of coastal marshes greenhouse gases emissions over China

Coastal marshes contribute greatly to atmospheric greenhouse gases (GHGs) and have thus attracted considerable attention at regional and global scales. However, the relative importance of climates and soil variables to the geographical variation of GHG emissions at a large geographical scale remains poorly understood. We thus investigated the spatial patterns of GHG (CO2, CH4 and N2O) emissions from coastal marshes along a latitudinal gradient in China and identified the climatic and soil variables controlling the emissions. Emissions of CO2, CH4 and N2O were estimated at rates of 12.8–310 mg CO2 m–2 h−1, 23.6–986 µg CH4 m–2 h−1 and 1.58–110 µg N2O m–2 h−1, respectively. The emissions of CH4, N2O and CO2 increased from high to low latitudes and were strongly correlated with climatic temperature. These emissions also varied significantly between temperate and subtropical climates and between winter and summer. Soil Eh, total organic carbon (TOC), dissolved organic carbon (DOC), total nitrogen, microbial biomass carbon (MBC) and nitrogen (MBN), bacterial abundance were the key regulators of CH4 emissions. Soil Eh, TOC, DOC, MBC and MBN were the crucial variables controlling N2O emissions. CO2 emissions across the coastal marshes were controlled mainly by soil pH, TOC, DOC, MBC, MBN and bacterial abundance. Although GHG emissions were strongly correlated with climatic temperature, 28–67% of the variations in GHG emissions were attributed to soil variables. The relative amplitudes (%) of the increases in GHG emissions, estimated on the basis of projected annual increases of 2 or 4 °C by 2100, were larger in temperate than subtropical coastal marshes, indicating that the coastal marshes with lower temperature were more sensitive to global warming. The annual emissions of CO2, CH4 and N2O estimated from the Chinese coastal marshes accounted for 6.6, 0.62 and 0.70‰ of the annual emissions from global terrestrial ecosystems, respectively. These results suggest that coastal marshes will be important to future global warming and will play a critical role in global GHG emissions.

THE EFECT OF CONFLICT MANAGEMENT STYLES ON EMPLOYEE PERFORMANCE AMONG LEBANESE INDUSTRIAL FIRMS

This paper studies the effect of conflict management on employees’ performance among some industrial firms in Lebanon. First, conceptual framework was developed based on extensive literature review where conflict management styles and employee performance were discussed comprehensively in terms of definitions and related previous studies. Due to the lack of empirical studies in the Lebanese industrial sector, this empirical study was conducted to test the proposed research hypotheses. Thus, a quantitative approach was adopted through conducting an online survey among 93 employees from three different industrial organizations specialized in producing construction materials, plastics, and wood and metal furniture in Lebanon. The response rate was limited due to COVID-19 Pandemic, where only 63 employees responded. Data analysis was conducted through SPSS. Descriptive statistics were obtained and research hypotheses were tested using Pearson correlation and regression analysis. The results obtained showed that in general, conflict management has positive and significant effect on employees’ performance but to a weak extent. Particularly, only collaborative, compromising and accommodating styles had weak positive correlation with employee performance. Future surveys may involve more companies on a larger geographical scale, and include additional number of workers, for the purpose of attaining more reliable results.

Genetic diversity in Iranian indigenous sheep vis-à-vis selected exogenous sheep breeds and wild mouflon.

The heterogeneity of climate and different agro-ecological conditions in Iran have resulted in development of 27 indigenous sheep breeds. Wild Asiatic mouflon (Ovis orientalis) is believed to be the ancestor of Iranian sheep. Evaluation of genetic diversity and population structure within and among domestic breeds has important implications for animal breeding programs and genetic resources management. Based on 50K SNP genotype data, we studied the genetic diversity of five indigenous Iranian sheep breeds: Afshari (n=37), Moghani (n=34), Qezel (n=35), Zel (n=46) and Lori-Bakhtiari (n=46), and Asiatic mouflon (n=8) sampled from Iran. Furthermore, genetic diversity and the breed admixture of Iranian sheep were assessed on a larger geographic scale using a reference panel comprising: three indigenous Afghan breeds - Arabi (n=15), Balouchi (n=15) and Gadik (n=15); three indigenous breeds from Turkey and Cyprus - Cyprus Fat Tail (n=30), Karakas (n=18) and Norduz (n=20); and three commercial European breeds - Suffolk (n=19), Comisana (n=24) and Engadine Red Sheep (n=24). The results revealed that the investigated breeds are divided into five genetically distinct clusters according to their geographic origin. Afshari was closest to the local mouflon population and showed signs of mouflon admixture. Qezel was identified as a hybrid sheep breed. Much evidence supported the Afghan breeds being identical. Inbreeding values, which were estimated based on ROHs, were highest for Suffolk (FROH =0.0544) and lowest for Balouchi (FROH =0.0078). In conclusion, analysis of selected breeds from neighboring countries along with Asiatic mouflon gave a deeper insight into the evolutionary history and origin of Iranian sheep with important implications for future breed management.

Outlier detection methods to improve the quality of citizen science data

Citizen science involves public participation in research, usually through volunteer observation and reporting. Data collected by citizen scientists are a valuable resource in many fields of research that require long-term observations at large geographic scales. However, such data may be perceived as less accurate than those collected by trained professionals. Here, we analyze the quality of data from a plant phenology network, which tracks biological response to climate change. We apply five algorithms designed to detect outlier observations or inconsistent observers. These methods rely on different quantitative approaches, including residuals of linear models, correlations among observers, deviations from multivariate clusters, and percentile-based outlier removal. We evaluated these methods by comparing the resulting cleaned datasets in terms of time series means, spatial data coverage, and spatial autocorrelations after outlier removal. Spatial autocorrelations were used to determine the efficacy of outlier removal, as they are expected to increase if outliers and inconsistent observations are successfully removed. All data cleaning methods resulted in better Moran's I autocorrelation statistics, with percentile-based outlier removal and the clustering method showing the greatest improvement. Methods based on residual analysis of linear models had the strongest impact on the final bloom time mean estimates, but were among the weakest based on autocorrelation analysis. Removing entire sets of observations from potentially unreliable observers proved least effective. In conclusion, percentile-based outlier removal emerges as a simple and effective method to improve reliability of citizen science phenology observations.

Profiling European countries on COVID–19 prevalence and association with non–pharmaceutical interventions

Introduction It is essential to understand, on a large geographical scale, the dimension of the COVID–19 pandemic by identifying the most affected countries, knowing that all the world is suffering an unusual disruption regarding several health impacts, but also heavy economic, financial and social effects. A key role is reserved to Data Science to understand the present and to deepen a prospective analysis at COVID–19 day after. Objective The main objective of the present study is to describe the COVID–19 prevalence in EU and five other OECD countries using five epidemiological variables. Secondly their association with non–pharmaceutical measures taken in some countries to control and attenuate the evolution of the epidemic was analyzed. Methods The COVID–19 study covers twenty–six EU countries and additionally Switzerland, Norway, Turkey, Israel and United Kingdom. Five epidemiologic variables were analyzed by 100.000 inhabitants at the beginning of May 2020: total number of cases, total number of deaths, total number of active cases, total number of critical or serious cases and total number of tests. Also, eight non–pharmaceutical measures were selected for association purposes. A multivariate statistical exploratory approach with principal components, hierarchical and non–hierarchical (k–means) cluster analyses was applied. Results A COVID–19 prevalence typology of four country clusters was identified regarding EU countries and five OECD countries on early May. In the two clusters, with a total of ten countries where the pandemic seemed to evolve more seriously, different patterns regarding the number of tests are observed. Two other clusters, with 12 and 9 countries, show an intermediate or low prevalence but differences in testing patterns. For EU countries of both clusters more affected, COVID–19 containment strategies were studied considering three modalities of implementation timing for eight non–pharmaceutical measures. The three different behaviors mirrored the clusters findings. Countries previously classified into cluster 1 appear together again, as do countries belonging to cluster 2. In spite of a common behavior for some measures, generally countries of cluster 2 implemented other interventions later in time. Sweden is a “special case”, taking just a few of these measures, most of them later than other countries.

Loss of pollinator specialization revealed by historical opportunistic data: Insights from network-based analysis.

We are currently facing a large decline in bee populations worldwide. Who are the winners and losers? Generalist bee species, notably those able to shift their diet to new or alternative floral resources, are expected to be among the least vulnerable to environmental change. However, studies of interactions between bees and plants over large temporal and geographical scales are limited by a lack of historical records. Here, we used a unique opportunistic century-old countrywide database of bee specimens collected on plants to track changes in the plant-bee interaction network over time. In each historical period considered, and using a network-based modularity analysis, we identified some major groups of species interacting more with each other than with other species (i.e. modules). These modules were related to coherent functional groups thanks to an a posteriory trait-based analysis. We then compared over time the ecological specialization of bees in the network by computing their degree of interaction within and between modules. “True” specialist species (or peripheral species) are involved in few interactions both inside and between modules. We found a global loss of specialist species and specialist strategies. This means that bee species observed in each period tended to use more diverse floral resources from different ecological groups over time, highly specialist species tending to enter/leave the network. Considering the role and functional traits of species in the network, combined with a long-term time series, provides a new perspective for the study of species specialization.

Development of a national childhood obesogenic environment index in the United States: differences by region and rurality

BackgroundDiverse environmental factors are associated with physical activity (PA) and healthy eating (HE) among youth. However, no study has created a comprehensive obesogenic environment index for children that can be applied at a large geographic scale. The purpose of this study was to describe the development of a childhood obesogenic environment index (COEI) at the county level across the United States.MethodsA comprehensive search of review articles (n = 20) and input from experts (n = 12) were used to identify community-level variables associated with youth PA, HE, or overweight/obesity for potential inclusion in the index. Based on strength of associations in the literature, expert ratings, expertise of team members, and data source availability, 10 key variables were identified – six related to HE (# per 1000 residents for grocery/superstores, farmers markets, fast food restaurants, full-service restaurants, and convenience stores; as well as percentage of births at baby (breastfeeding)-friendly facilities) and four related to PA (percentage of population living close to exercise opportunities, percentage of population < 1 mile from a school, a composite walkability index, and number of violent crimes per 1000 residents). Data for each variable for all counties in the U.S. (n = 3142) were collected from publicly available sources. For each variable, all counties were ranked and assigned percentiles ranging from 0 to 100. Positive environmental variables (e.g., grocery stores, exercise opportunities) were reverse scored such that higher values for all variables indicated a more obesogenic environment. Finally, for each county, a total obesogenic environment index score was generated by calculating the average percentile for all 10 variables.ResultsThe average COEI percentile ranged from 24.5–81.0 (M = 50.02,s.d. = 9.01) across US counties and was depicted spatially on a choropleth map. Obesogenic counties were more prevalent (F = 130.43,p < .0001) in the South region of the U.S. (M = 53.0,s.d. = 8.3) compared to the Northeast (M = 43.2,s.d. = 6.9), Midwest (M = 48.1,s.d. = 8.5), and West (M = 48.4,s.d. = 9.8). When examined by rurality, there were also significant differences (F = 175.86,p < .0001) between metropolitan (M = 46.5,s.d. = 8.4), micropolitan (M = 50.3,s.d. = 8.1), and rural counties (M = 52.9,s.d. = 8.8) across the U.S.ConclusionThe COEI can be applied to benchmark obesogenic environments and identify geographic disparities and intervention targets. Future research can examine associations with obesity and other health outcomes.

Geographic Variation in Floral Color and Reflectance Correlates With Temperature and Colonization History.

Petal color variation within species is common and may be molded by abiotic or biotic selection pressures, or neutral population structure. For example, darker flowers may be favored in cooler environments because they absorb more solar radiation, elevating the temperature of reproductive structures. Additionally, flower color may evolve to attract the dominant or most efficient pollinator type in a given population. Here, we evaluate geographic variation in petal coloration across the range of Campanula americana in Eastern North America and test whether color covaries with abiotic factors, the pollination community, and genetic structure established through post-glacial expansion. Consistent with other studies, flowers from cooler, higher latitude populations were less reflective across the UV-NIR spectrum than those from warmer populations. Local temperature explained variation in petal reflectance better than the pollinator community or colonization history. Petal color perceived by trichromatic bee pollinators displayed a strong longitudinal pattern but was unassociated with climatic factors and the pollinator community. Instead, pollinator-perceived color was tightly correlated with the geographic distance from C. americana's glacial refugium. In total, abiotic conditions appear to shape large-scale geographic variation in the intensity of petal reflectance while genetic structure is the strongest driver of pollinator-perceived petal coloration. This study highlights the importance of abiotic factors and historical processes associated with range expansion as major evolutionary forces shaping diversity of flower coloration on large geographic scales.

The influence of immune activation on thermal tolerance along a latitudinal cline.

Global change is shifting both temperature patterns and the geographic distribution of pathogens, and infection has already been shown to substantially reduce host thermal performance, potentially placing populations at greater risk that previously thought. But what about individuals that are able to successfully clear an infection? Whilst the direct damage a pathogen causes will likely lead to reductions in host's thermal tolerance, the response to infection often shares many underlying pathways with the general stress response, potentially acting as a buffer against subsequent thermal stress. Here, by exposing Drosophila melanogaster to heat-killed bacterial pathogens, we investigate how activation of a host's immune system can modify any response to both heat and cold temperature stress. In a single focal population, we find that immune activation can improve a host's knockdown times during heat shock, potentially offsetting some of the damage that would subsequently arise as an infection progresses. Conversely, immune activation had a detrimental effect on CTmax and did not influence lower thermal tolerance as measured by chill-coma recovery time. However, we also find that the influence of immune activation on heat knockdown times is not generalizable across an entire cline of locally adapted populations. Instead, immune activation led to signals of local adaptation to temperature being lost, erasing the previous advantage that populations in warmer regions had when challenged with heat stress. Our results suggest that activation of the immune system may help buffer individuals against the detrimental impact of infection on thermal tolerance; however, any response will be population specific and potentially not easily predicted across larger geographic scales, and dependent on the form of thermal stress faced by a host.

Antibiotic resistomes in drinking water sources across a large geographical scale: Multiple drivers and co-occurrence with opportunistic bacterial pathogens

Antibiotic resistance genes (ARGs) can survive the water treatment process. However, the prevalence patterns, key drivers, and relationships with opportunistic pathogens of the antibiotic resistome harbored in drinking water sources remain unclear. Herein, 53 drinking water samples collected across a large geographical scale in China were characterized based on ARGs, mobile genetic elements (MGEs), bacterial communities, antibiotics, and opportunistic bacterial pathogens. A total of 265 unique ARGs and MGEs were detected by high-throughput quantitative polymerase chain reaction (HT-qPCR), and 101 genes were shared among over 50% of samples. ARG abundance was higher in rivers than in reservoirs or groundwater, and ARG similarity showed a distance-decay relationship at the >4 000 km scale. Four out of the five detected opportunistic pathogens (i.e., Escherichia coli, Mycobacterium spp., Clostridium perfringens, and Bacillus cereus group) were potential hosts of ARGs. Based on multivariate statistics, our results demonstrated that the factors influencing the antibiotic resistome in drinking water sources were multiple and interactive. The bacterial community greatly contributed to ARG structure, and antibiotic concentrations and MGEs also affected ARG proliferation. The structural equation model indicated that geographical location and sample types (i.e., river, reservoir, and groundwater) had indirect effects on ARGs by changing the bacterial community and antibiotic concentration. Holistic consideration of natural and anthropogenic factors is recommended to understand antibiotic resistome variation in drinking water sources at a large geographical scale. Furthermore, large-scale diverse samples are suggested to minimize the potential influence of accident or stochasticity. Our findings provide insight into water quality risks induced by drinking water antibiotic resistomes.

How do microbiota associated with an invasive seaweed vary across scales?

Communities are shaped by scale dependent processes. To study the diversity and variation of microbial communities across scales, the invasive and widespread seaweed Agarophyton vermiculophyllum presents a unique opportunity. We characterized pro- and eukaryotic communities associated with this holobiont across its known distribution range, which stretches over the northern hemisphere. Our data reveal that community composition and diversity in the holobiont vary at local but also larger geographic scales. While processes acting at the local scale (i.e., within population) are the main structuring drivers of associated microbial communities, changes in community composition also depend on processes acting at larger geographic scales. Interestingly, the largest analysed scale (i.e., native and non-native ranges) explained variation in the prevalence of predicted functional groups, which could suggest a functional shift in microbiota occurred over the course of the invasion process. While high variability in microbiota at the local scale supports A.vermiculophyllum to be a generalist host, we also identified a number of core taxa. These geographically independent holobiont members imply that cointroduction of specific microbiota may have additionally promoted the invasion process.

Estimation of Hourly near Surface Air Temperature Across Israel Using an Ensemble Model

Mapping of near-surface air temperature (Ta) at high spatio-temporal resolution is essential for unbiased assessment of human health exposure to temperature extremes, not least given the observed trend of urbanization and global climate change. Data constraints have led previous studies to focus merely on daily Ta metrics, rather than hourly ones, making them insufficient for intra-day assessment of health exposure. In this study, we present a three-stage machine learning-based ensemble model to estimate hourly Ta at a high spatial resolution of 1 × 1 km2, incorporating remotely sensed surface skin temperature (Ts) from geostationary satellites, reanalysis synoptic variables, and observations from weather stations, as well as auxiliary geospatial variables, which account for spatio-temporal variability of Ta. The Stage 1 model gap-fills hourly Ts at 4 × 4 km2 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which are subsequently fed into the Stage 2 model to estimate hourly Ta at the same spatio-temporal resolution. The Stage 3 model downscales the residuals between estimated and measured Ta to a grid of 1 × 1 km2, taking into account additionally the monthly diurnal pattern of Ts derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In each stage, the ensemble model synergizes estimates from the constituent base learners—random forest (RF) and extreme gradient boosting (XGBoost)—by applying a geographically weighted generalized additive model (GAM), which allows the weights of results from individual models to vary over space and time. Demonstrated for Israel for the period 2004–2017, the proposed ensemble model outperformed each of the two base learners. It also attained excellent five-fold cross-validated performance, with overall root mean square error (RMSE) of 0.8 and 0.9 °C, mean absolute error (MAE) of 0.6 and 0.7 °C, and R2 of 0.95 and 0.98 in Stage 1 and Stage 2, respectively. The Stage 3 model for downscaling Ta residuals to 1 km MODIS grids achieved overall RMSE of 0.3 °C, MAE of 0.5 °C, and R2 of 0.63. The generated hourly 1 × 1 km2 Ta thus serves as a foundation for monitoring and assessing human health exposure to temperature extremes at a larger geographical scale, helping to further minimize exposure misclassification in epidemiological studies.